{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:BLBMRXFVW2XUQKFBYAPM4YFOJT","short_pith_number":"pith:BLBMRXFV","schema_version":"1.0","canonical_sha256":"0ac2c8dcb5b6af4828a1c01ece60ae4ccfeb63f9aa257ee4425fb967e83d397b","source":{"kind":"arxiv","id":"2412.18827","version":1},"attestation_state":"computed","paper":{"title":"PhyloGen: Language Model-Enhanced Phylogenetic Inference via Graph Structure Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"q-bio.PE","authors_text":"Chenrui Duan, Siyuan Li, Stan Z. Li, Yongjie Xu, Zelin Zang","submitted_at":"2024-12-25T08:33:05Z","abstract_excerpt":"Phylogenetic trees elucidate evolutionary relationships among species, but phylogenetic inference remains challenging due to the complexity of combining continuous (branch lengths) and discrete parameters (tree topology). Traditional Markov Chain Monte Carlo methods face slow convergence and computational burdens. Existing Variational Inference methods, which require pre-generated topologies and typically treat tree structures and branch lengths independently, may overlook critical sequence features, limiting their accuracy and flexibility. We propose PhyloGen, a novel method leveraging a pre-"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2412.18827","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.PE","submitted_at":"2024-12-25T08:33:05Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1bfbd69a9dbf5fb224e11c34df7d2f33ad45a344a592a3039b260ee4efa9946c","abstract_canon_sha256":"15ff64ddfdba0dd29f5b6de45179df10498980f73a166bbbd7fc0e4d6fe9c68d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:54:11.447089Z","signature_b64":"dZYftbtH8jRJrtRXrmdXzbS5E3+ISWrnuYjaXgYlLg5sf6HvfgePVSPgyXj2zL9Xq9RDX9WTFAJaXAHsJW+zCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0ac2c8dcb5b6af4828a1c01ece60ae4ccfeb63f9aa257ee4425fb967e83d397b","last_reissued_at":"2026-07-05T09:54:11.446615Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:54:11.446615Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PhyloGen: Language Model-Enhanced Phylogenetic Inference via Graph Structure Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"q-bio.PE","authors_text":"Chenrui Duan, Siyuan Li, Stan Z. Li, Yongjie Xu, Zelin Zang","submitted_at":"2024-12-25T08:33:05Z","abstract_excerpt":"Phylogenetic trees elucidate evolutionary relationships among species, but phylogenetic inference remains challenging due to the complexity of combining continuous (branch lengths) and discrete parameters (tree topology). Traditional Markov Chain Monte Carlo methods face slow convergence and computational burdens. Existing Variational Inference methods, which require pre-generated topologies and typically treat tree structures and branch lengths independently, may overlook critical sequence features, limiting their accuracy and flexibility. We propose PhyloGen, a novel method leveraging a pre-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.18827","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2412.18827/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2412.18827","created_at":"2026-07-05T09:54:11.446673+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.18827v1","created_at":"2026-07-05T09:54:11.446673+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.18827","created_at":"2026-07-05T09:54:11.446673+00:00"},{"alias_kind":"pith_short_12","alias_value":"BLBMRXFVW2XU","created_at":"2026-07-05T09:54:11.446673+00:00"},{"alias_kind":"pith_short_16","alias_value":"BLBMRXFVW2XUQKFB","created_at":"2026-07-05T09:54:11.446673+00:00"},{"alias_kind":"pith_short_8","alias_value":"BLBMRXFV","created_at":"2026-07-05T09:54:11.446673+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.21859","citing_title":"PhylaFlow: Hybrid Flow Matching in Billera-Holmes-Vogtmann Tree Space for Phylogenetic Inference","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BLBMRXFVW2XUQKFBYAPM4YFOJT","json":"https://pith.science/pith/BLBMRXFVW2XUQKFBYAPM4YFOJT.json","graph_json":"https://pith.science/api/pith-number/BLBMRXFVW2XUQKFBYAPM4YFOJT/graph.json","events_json":"https://pith.science/api/pith-number/BLBMRXFVW2XUQKFBYAPM4YFOJT/events.json","paper":"https://pith.science/paper/BLBMRXFV"},"agent_actions":{"view_html":"https://pith.science/pith/BLBMRXFVW2XUQKFBYAPM4YFOJT","download_json":"https://pith.science/pith/BLBMRXFVW2XUQKFBYAPM4YFOJT.json","view_paper":"https://pith.science/paper/BLBMRXFV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.18827&json=true","fetch_graph":"https://pith.science/api/pith-number/BLBMRXFVW2XUQKFBYAPM4YFOJT/graph.json","fetch_events":"https://pith.science/api/pith-number/BLBMRXFVW2XUQKFBYAPM4YFOJT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BLBMRXFVW2XUQKFBYAPM4YFOJT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BLBMRXFVW2XUQKFBYAPM4YFOJT/action/storage_attestation","attest_author":"https://pith.science/pith/BLBMRXFVW2XUQKFBYAPM4YFOJT/action/author_attestation","sign_citation":"https://pith.science/pith/BLBMRXFVW2XUQKFBYAPM4YFOJT/action/citation_signature","submit_replication":"https://pith.science/pith/BLBMRXFVW2XUQKFBYAPM4YFOJT/action/replication_record"}},"created_at":"2026-07-05T09:54:11.446673+00:00","updated_at":"2026-07-05T09:54:11.446673+00:00"}